Abstract
Brain-Computer Interface (BCI) aims to improve the detection and decoding of brain signals acquired by electroencephalogram (EEG). In the recent years, artificial intelligence development has stepped onto a new stage, which boosts the research of BCI. This paper focuses on implementation of BCI by recognition of imagined digits. The subject was asked to imagine a digit 0 or 1 without other stimulation. The experiment was conducted using a 14 electrode Electroencephalogram. The subject was asked to imagine a digit for 30 s and the signals were recorded for analysis. Based on the results of the classification and ERP analysis, the O1 and O2 electrode positions (10–20 system) were chosen. Four methods were proposed for the feature extraction by using Event Related Potential (ERP) analysis, Power Spectral Density Analysis (PSD), Independent Component Analysis (ICA) and Common Spatial Pattern (CSP). Finally, several classification methods were used to recognize the imagined digits based on the extracted features. Experimental results showed that the results obtained by Artificial Neural Network (ANN) after CSP performed the best. The classification accuracy achieves 66.88%.
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Acknowledgement
This work was supported by the Science and Technology Program of Guangzhou (201804010271); National Natural Science Foundation of China (61872034, 61572067); Natural Science Foundation of Guizhou Province ([2019]1064); Special Innovative Projects in Key Platforms and Scientific Research Projects of Guangdong Universities in 2018.
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Harsono, M., Liang, Lq., Zheng, Xw., Jesse, F.F., Cen, Yg., Jin, W. (2019). Classification of Imagined Digits via Brain-Computer Interface Based on Electroencephalogram. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_44
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DOI: https://doi.org/10.1007/978-981-13-9917-6_44
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